Analysis of small three-dimensional fluorescence spectroscopy dataset using migration learning an example of phenol concentration prediction in wastewater.

Three-dimensional fluorescence spectroscopy has been widely used to detect organic pollutants in water. However, the amount of data required for three-dimensional fluorescence spectroscopy analysis is relatively large, and the time cost of sample collection is high. The amount of data has become an unavoidable limitation of spectral analysis. This study takes the detection of phenol in industrial discharge wastewater as an example and proposes a transfer learning method for small fluorescence spectroscopy datasets. First, fluorescence spectra are generated by splitting them into linear combinations of positively and negatively distributed spectra. Then, based on the idea of transfer learning, the generated fluorescence spectra are used to train a task-specific pre-trained model, which is then transferred to the collected spectral dataset. Experimental results show that the prediction performance of the transfer learning method is improved by 50.08 % compared with that obtained by directly training the model using a small amount of spectral data. In addition, when the spectral data remains unchanged, the accuracy of the model can be improved to a certain extent by increasing the amount of spectral data used for pre-training. The transfer learning method proposed in this study further improves the prediction accuracy when data is limited, and the results of verification in real environments are also satisfactory. It provides a feasible solution to the problem of data limitations in three-dimensional fluorescence spectroscopy.

A novel laccase-like Cu-MOF for colorimetric differentiation and detection of phenolic compounds.

The development of convenient, fast, and cost-effective methods for differentiating and detecting common organic pollutant phenols has become increasingly important for environmental and food safety. In this study, a copper metal-organic framework (Cu-MOF) with flower-like morphology was synthesized using 2-methylimidazole (2-MI) as ligands. The Cu-MOF was designed to mimic the natural laccase active site and proved demonstrated excellent mimicry of enzyme-like activity. Leveraging the superior properties of the constructed Cu-MOF, a colorimetric method was developed for analyzing phenolic compounds. This method exhibited a wide linear range from 0.1 to 100 µM with a low limit of detection (LOD) of 0.068 µM. Besides, by employing principal component analysis (PCA), nine kinds of phenols was successfully distinguished and identified. Moreover, the combination of smartphones with RGB profiling enabled real-time, quantitative, and high-throughput detection of phenols. Therefore, this work presents a paradigm and offers guidance for the differentiation and detection of phenolic pollutants in the environment.

Determination of Phenol with Peroxidase Immobilized on CaCO3.

Phenols are widely used in industries despite their toxicity, which requires governments to limit their concentration in water to 5 mg/L before discharge to the city sewer. Thus, it is essential to develop a rapid, simple, and low-cost detection method for phenol. This study explored two pathways of peroxidase immobilization to develop a phenol detection system: peroxidase encapsulation into polyelectrolyte microcapsules and peroxidase captured by CaCO3. The encapsulation of peroxidase decreased enzyme activity by 96%; thus, this method cannot be used for detection systems. The capturing process of peroxidase by CaCO3 microspherulites did not affect the maximum reaction rate and the Michaelis constant of peroxidase. The native peroxidase-Vmax = 109 µM/min, Km = 994 µM; CaCO3-peroxidase-Vmax = 93.5 µM/min, Km = 956 µM. Ultimately, a reusable phenol detection system based on CaCO3 microparticles with immobilized peroxidase was developed, capable of detecting phenol in the range of 700 ng/mL to 14 µg/mL, with an error not exceeding 5%, and having a relatively low cost and production time. The efficiency of the system was confirmed by determining the content of phenol in a paintwork product.

Quantitative detection of phenol in wastewater using square wave voltammetry with pre-concentration.

Phenol is a common pollutant found in wastewater, and its allowable discharge limit is 0.5 parts-per-million (ppm). Therefore, it is critical to monitor phenol in the sub-ppm range with high sensitivity and a low limit of detection. Herein, we report a quantitative method for detecting phenol in industrial wastewater through square wave voltammetry (SWV), in which phenol is oxidized to phenoxyl radicals and then became catechol and hydroquinone for detection. By using this method, phenol in the sub-ppm range can be detected reliably over a wide pH range. The sensitivity can be further improved by using a pre-concentration step for phenol before scanning. The method has a limit of detection of 0.1 ppb for phenol. Finally, three graphite electrodes were applied as working, counter and reference electrodes, respectively, in a millifluidic device for continuous detection of phenol in industrial wastewater flowing at 300 µL/min. Because of its simplicity, the sensor can be mass-produced and deployed on a large scale to monitor phenol in industrial wastewater.

DNA-copper hybrid nanoflowers as efficient laccase mimics for colorimetric detection of phenolic compounds in paper microfluidic devices.

Laccases are important multicopper oxidases that are involved in many biotechnological processes; however, they suffer from poor stability as well as high cost for production/purification. Herein, we found that DNA-copper hybrid nanoflowers, prepared via simple self-assembly of DNA and copper ions, exhibit an intrinsic laccase-mimicking activity, which is significantly higher than that of control materials formed in the absence of DNA. Upon testing all four nucleobases, we found that hybrid nanoflowers composed of guanine-rich ssDNA and copper phosphate (GNFs) showed the highest catalytic activity, presumably due to the affirmative coordination between guanine and copper ions. At the same mass concentration, GNFs had similar Km but 3.5-fold higher Vmax compared with those of free laccase, and furthermore, they exhibited significantly-enhanced stability in ranges of pH, temperature, ionic strength, and incubation period of time. Based on these advantageous features, GNFs were applied to paper microfluidic devices for colorimetric detection of diverse phenolic compounds such as dopamine, catechol, and hydroquinone. In the presence of phenolic compounds, GNFs catalyzed their oxidation to react with 4-aminoantipyrine for producing a colored adduct, which was conveniently quantified from an image acquired using a conventional smartphone with ImageJ software. Besides, GNFs successfully catalyzed the decolorization of neutral red dye much faster than free laccase. This work will facilitate the development of nanoflower-type nanozymes for a wide range of applications in biosensors and bioremediation.

Promotion effect of Zn on 2D bimetallic NiZn metal organic framework nanosheets for tyrosinase immobilization and ultrasensitive detection of phenol.

Environmental monitoring of pollutants is essential to guarantee the human health and maintain the ecosystem. The exploration of both simple and sensitive detection method has aroused widespread attentions. Herein, 2D bimetallic metal organic framework nanosheets (NiZn-MOF NSs) with tunable Ni/Zn ratios were synthesized, and for the first time employed to construct a tyrosinase biosensor. It is revealed that Zn element not only tuned the porosity structure and electronic structure of MOF NSs, but also modified their electrochemical activity. As a result, enzyme immobilization and electrochemical sensing performance of the NiZn-MOF NSs based biosensor were significantly enhanced by a suitable Zn addition. The fabricated tyrosinase biosensor exhibited excellent analytical detections, with a wide linear range from 0.08 µM to 58.2 µM, a high sensitivity of 159.3 mA M-1, and an ultralow detection limit of 6.5 nM. In addition, the proposed biosensing approach also demonstrated good repeatability, superior selectivity, long-term stability, and high recovery for phenol detection in the real tap water samples.

Amperometric Biosensor Based on Zirconium Oxide/Polyethylene Glycol/Tyrosinase Composite Film for the Detection of Phenolic Compounds.

A phenolic biosensor based on a zirconium oxide/polyethylene glycol/tyrosinase composite film for the detection of phenolic compounds has been explored. The formation of the composite film was expected via electrostatic interaction between hexacetyltrimethylammonium bromide (CTAB), polyethylene glycol (PEG), and zirconium oxide nanoparticles casted on screen printed carbon electrode (SPCE). Herein, the electrode was treated by casting hexacetyltrimethylammonium bromide on SPCE to promote a positively charged surface. Later, zirconium oxide was mixed with polyethylene glycol and the mixture was dropped cast onto the positively charged SPCE/CTAB. Tyrosinase was further immobilized onto the modified SPCE. Characterization of the prepared nanocomposite film and the modified SPCE surface was investigated by scanning electron microscopy (SEM), Electrochemical Impedance Spectroscopy (EIS), and Cyclic voltamogram (CV). The developed biosensor exhibits rapid response for less than 10 s. Two linear calibration curves towards phenol in the concentrations ranges of 0.075-10 µM and 10-55 µM with the detection limit of 0.034 µM were obtained. The biosensor shows high sensitivity and good storage stability for at least 30 days.

Electrophoresis-chemiluminescence detection of phenols catalyzed by hemin.

Based on the catalytic activity of hemin, an efficient biocatalyst, an indirect capillary electrophoresis-chemiluminescence (CE-CL) detection method for phenols using a hemin-luminol-hydrogen peroxide system was developed. Through a series of static injection experiments, hemin was found to perform best in a neutral solution rather than an acidic or alkaline medium. Although halide ions such as Br(-) and F(-) could further enhance the CL signal catalyzed by hemin, it is difficult to apply these conditions to this CE-CL detection system because of the self-polymerization of hemin, as it hinders the CE process. The addition of concentrated ammonium hydroxide to an aqueous/dimethyl sulfoxide solution of hemin-luminol afforded a stable CE-CL baseline. The indirect CE-CL detection of five phenols using this method gave the following limits of detections: 4.8 × 10(-8) mol/L (o-sec-butylphenol), 4.9 × 10(-8) mol/L (o-cresol), 5.4 × 10(-8) mol/L (m-cresol), 5.3 × 10(-8) mol/L (2,4-dichlorophenol) and 7.1 × 10(-8) mol/L (phenol).

Three-dimensional graphene micropillar based electrochemical sensor for phenol detection.

A three-dimensional (3D) graphene incorporated electrochemical sensor was constructed for sensitive enzyme based phenol detection. To form the 3D graphene structure, polydimethylsiloxane (PDMS) micropillars were fabricated in the microchannel by using a conventional photolithography and the surface was modified with 3-aminopropyltriethoxysilane. Then, the negatively charged graphene oxide sheets were electrostatically adsorbed on the PDMS micropillar surface, and reduced in the hydrazine vapor. The resultant 3D graphene film provides a conductive working electrode as well as an enzyme-mediated sensor with a large surface area. After bonded with an electrode patterned glass wafer, the 3D graphene based electrochemical sensor was produced. Using the 3D graphene as a working electrode, an excellent electron transfer property was demonstrated by cyclic voltammetry measurement in an electrolyte solution containing 1mM K3Fe(CN)6 and 0.1 M KCl. To utilize the 3D graphene as an enzyme sensor, tyrosinase enzymes were immobilized on the surface of the graphene micropillar, and the target phenol was injected in the microchannel. The enzyme catalytic reaction process was monitored by amperometric responses and the limit of detection for phenol was obtained as 50 nM, thereby suggesting that the 3D graphene micropillar structure enhances the enzyme biosensing capability not only by increasing the surface area for enzyme immobilization, but also by the superlative graphene conductivity property.